An Inverse Reinforcement Learning Approach to Car Following Behaviors

In this study we provide new insights into the classic car-following theories by learning drivers’ behavioral preferences. We model car-following behavior using decision-theoretic techniques. We assume the driver is a decision maker acting based on a utility function that assigns the degree of desirability of the driving situation. Our method is to use inverse problem in control theory, also known as inverse reinforcement learning in a more modern terminology in machine learning. We use a publically available dataset on the car-following behavior known as the Bosch dataset, which includes headway distance, speed and acceleration data. Our simulation results discover the reward function that makes the actual driving behavior in the data preferable to any other behavior. Understanding such behaviors and preferences is becoming crucial as we are entering the modern era of transportation automation. Considering drivers’ preferences while designing for automation features would improve the safety and efficiency of the driving environment while ensuring desirable and comfortable setting for those inside the vehicles.